Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Fast reaction, slow diffusion, and curve shortening
SIAM Journal on Applied Mathematics
Singularities and similarities in interface flows
Trends and perspectives in applied mathematics
Efficient algorithms for diffusion-generated motion by mean curvature
Journal of Computational Physics
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
International Journal of Computer Vision
Segmentation with Depth but Without Detecting Junctions
Journal of Mathematical Imaging and Vision
Threshold dynamics for the piecewise constant Mumford-Shah functional
Journal of Computational Physics
Diffusion generated motion of curves on surfaces
Journal of Computational Physics
Image segmentation using a multilayer level-set approach
Computing and Visualization in Science
The Split Bregman Method for L1-Regularized Problems
SIAM Journal on Imaging Sciences
Diffusion generated motion using signed distance functions
Journal of Computational Physics
Improving density estimation by incorporating spatial information
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
IEEE Transactions on Image Processing
Image segmentation and selective smoothing by using Mumford-Shah model
IEEE Transactions on Image Processing
Inpainting of Binary Images Using the Cahn–Hilliard Equation
IEEE Transactions on Image Processing
A Wavelet-Laplace Variational Technique for Image Deconvolution and Inpainting
IEEE Transactions on Image Processing
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Our goal is to estimate a probability density based on discrete point data via segmentation techniques. Since point data may represent certain activities, such as crime, our method can be successfully used for detecting regions of high activity. In this work we design a binary segmentation version of the well-known Maximum Penalized Likelihood Estimation (MPLE) model, as well as a minimization algorithm based on thresholding dynamics originally proposed by Merriman et al. (The Computational Crystal Growers, pp. 73---83, 1992). We also present some computational examples, including one with actual residential burglary data from the San Fernando Valley.